1 Overview

We are strong supporters of open-source tools for reproducible research. Priority index or Pi is developed as a genomic-led target prioritisation system, with the focus on leveraging genetic data to prioritise targets at the gene and pathway level. Based on evidence of genetic association from GWAS data, this prioritisation system is able to resolve modulated genes (seed genes) by utilising knowledge of linkage disequilibrium (co-inherited variants), distance from the gene, and evidence of genetic association with gene expression. Seed genes are scored in an integrative way quantifying the genetic influence. Scored seed genes are subsequently used as baits to rank seed genes plus additional (non-seed) genes; this is achieved by iteratively exploring the global connectivity of a gene interaction network. Genes with the top priority are further used to identify/prioritise pathways that are significantly enriched with highly prioritised genes. Prioritised genes are also used to identify a gene network interconnecting many highly prioritised genes but a few lowly prioritised genes as linkers.

In the Showcases section, we illustrate the power of Pi to perform disease-centric genetic target prioritisation at the gene, pathway and network level using several inflammatory diseases as exemplars, including Ankylosing Spondylitis, Spondyloarthritis, and Systemic Lupus Erythematosus.

In the Ontology annotations & TPN nominations section, we integrate ontology annotations and TPN nominations as an additional support for disease-specific genetic prioritisation by Pi.

In the Cross-disease comparisons section, we summarise and compare results of genetic target prioritisation across diseases, done at the gene, pathway and network level.

2 Web app

A web-based interface of Pi is available here, which allows on-the-fly prioritisation for a user-defined SNP list.

3 Workflow

4 R functions

This intends for end-users who are comfortable with R (with the latest version available at http://cran.r-project.org). Priority functions are all contained in a new package called Pi which can be installed following instructions below:

# install the dependant packages including `XGR` and other packages used here
source("http://bioconductor.org/biocLite.R")
biocLite(c("XGR","devtools","VennDiagram"), siteRepos=c("http://cran.r-project.org"))
library(devtools)
install_github(c("hfang-bristol/XGR", "hfang-bristol/dnet"), dependencies=T)

# also, install the `Pi` package itself
library(devtools)
install_github(c("hfang-bristol/Pi"), dependencies=T)

Priority functions are designed in a nested way. The core relation follows this route: xPrioritiserSNPs -> xPrioritiserGenes -> xPrioritiser -> xRWR, achieving gene-level prioritisation from an input list of SNPs (along with their significant level). The output of this route is taken as the input of either xPrioritiserManhattan for visualising gene priority scores, xPrioritiserPathways for prioritising pathways, or xPrioritiserSubnet for identifying a network of top prioritised genes.

4.1 xRWR

xRWR: implements Random Walk with Restart (RWR) estimating the affinity score of nodes in a graph to a list of seed nodes. The affinity score can be viewed as the influential impact over the graph that is collectively imposed by seed nodes. If the seeds are not given, it will pre-compute affinity matrix for nodes in the input graph with respect to each starting node (as a seed) by looping over every node in the graph. A higher-level function xPrioritiser directly relies on it.

4.2 xPrioritiser

xPrioritiser: uses RWR to calculate the affinity score of nodes in a graph to a list of seed nodes. A node that has a higher affinity score to seed nodes will receive a higher priority score. It is an internal function acting as a general template for RWR-based prioritisation. A higher-level function xPrioritiserGenes directly relies on it.

4.3 xPrioritiserGenes

xPrioritiserGenes: prioritises gene targets from an input gene interaction network and the score info imposed on its seed nodes/genes. This function can be considered to be a specific instance of xPrioritiser, that is, specifying a gene interaction network as a graph and seed nodes as seed genes.

There are two sources of gene interaction network information: the STRING database (Szklarczyk et al. 2015) and the Pathways Commons database (Cerami et al. 2011). STRING is a meta-integration of undirect interactions from a functional aspect, while Pathways Commons mainly contains both undirect and direct interactions from a physical/pathway aspect. In addition to interaction type, users can choose the interactions of varying quality:

Code	Interaction (type and quality)	Database
STRING_high	Functional interactions (with high confidence scores>=700)	STRING
STRING_medium	Functional interactions (with medium confidence scores>=400)	STRING
PCommonsUN_high	Physical/undirect interactions (with references & >=2 sources)	Pathways Commons
PCommonsUN_medium	Physical/undirect interactions (with references & >=1 sources)	Pathways Commons
PCommonsDN_high	Pathway/direct interactions (with references & >=2 sources)	Pathways Commons
PCommonsDN_medium	Pathway/direct interactions (with references & >=1 sources)	Pathways Commons

4.4 xPrioritiserSNPs

xPrioritiserSNPs: prioritises gene targets from an input gene interaction network and a given list of SNPs together with the significance level (eg GWAS reported p-values). To do so, it first defines seed genes and their scores that are calculated in an integrative manner to quantify the genetic influence under SNPs. Genetic influential score on a seed gene is calculated from the SNP score (reflecting the SNP significant level), the gene-to-SNP distance weight and the eQTL mapping weight. This function can be considered to be a specific instance of xPrioritiserGenes, that is, specifying seed genes plus their scores.

Knowledge of co-inherited variants is also used to include additional SNPs that are in Linkage Disequilibrium (LD) with GWAS lead SNPs. LD SNPs are calculated based on 1000 Genomes Project data (1000 Genomes Project Consortium 2012). LD SNPs are defined to be any SNPs having R2 > 0.8 with GWAS lead SNPs. The user can choose the population used to calculate LD SNPs:

Code	Population	Project
AFR	African	1000 Genomes Project
AMR	Admixed American	1000 Genomes Project
EAS	East Asian	1000 Genomes Project
EUR	European	1000 Genomes Project
SAS	South Asian	1000 Genomes Project

4.5 xPrioritiserManhattan

xPrioritiserManhattan: visualises prioritised genes using manhattan plot, in which priority for genes is displayed on the Y-axis along with genomic locations on the X-axis. Also highlighted are genes with the top priority.

4.6 xPrioritiserPathways

xPrioritiserPathways: prioritises pathways based on enrichment analysis of genes with the top priority (eg top 100 genes) using a compendium of pathways. A highly prioritised pathway has its member genes with high gene-level priority scores, that is, having evidence of direct modulation by disease-associated lead (or LD) SNPs, and/or having evidence of indirect modulation at the network level.

In addition to pathways, enrichment analysis can be extended to other ontologies, as listed below:

Code	Ontology	Category
DO	Disease Ontology	Disease
GOMF	Gene Ontology Molecular Function	Function
GOBP	Gene Ontology Biological Process	Function
GOCC	Gene Ontology Cellular Component	Function
HPPA	Human Phenotype Phenotypic Abnormality	Phenotype
HPMI	Human Phenotype Mode of Inheritance	Phenotype
HPCM	Human Phenotype Clinical Modifier	Phenotype
HPMA	Human Phenotype Mortality Aging	Phenotype
MP	Mammalian/Mouse Phenotype	Phenotype
DGIdb	DGI druggable gene categories	Druggable
SF	SCOP domain superfamilies	Domain
PS2	phylostratific age information (our ancestors)	Evolution
MsigdbH	Hallmark gene sets	Hallmark (MsigDB)
MsigdbC1	Chromosome and cytogenetic band positional gene sets	Cytogenetics (MsigDB)
MsigdbC2CGP	Chemical and genetic perturbation gene sets	Perturbation (MsigDB)
MsigdbC2CPall	All pathway gene sets	Pathways (MsigDB)
MsigdbC2CP	Canonical pathway gene sets	Pathways (MsigDB)
MsigdbC2KEGG	KEGG pathway gene sets	Pathways (MsigDB)
MsigdbC2REACTOME	Reactome pathway gene sets	Pathways (MsigDB)
MsigdbC2BIOCARTA	BioCarta pathway gene sets	Pathways (MsigDB)
MsigdbC3TFT	Transcription factor target gene sets	TF targets (MsigDB)
MsigdbC3MIR	microRNA target gene sets	microRNA targets (MsigDB)
MsigdbC4CGN	Cancer gene neighborhood gene sets	Cancer (MsigDB)
MsigdbC4CM	Cancer module gene sets	Cancer (MsigDB)
MsigdbC5BP	GO biological process gene sets	Function (MsigDB)
MsigdbC5MF	GO molecular function gene sets	Function (MsigDB)
MsigdbC5CC	GO cellular component gene sets	Function (MsigDB)
MsigdbC6	Oncogenic signature gene sets	Oncology (MsigDB)
MsigdbC7	Immunologic signature gene sets	Immunology (MsigDB)

4.7 xPrioritiserSubnet

xPrioritiserSubnet: identifies a gene network that contains as many highly prioritised genes as possible but a few lowly prioritised genes as linkers. An iterative procedure of choosing different priority cutoff is also used to identify the network with a desired number of nodes/genes.

5 Showcases

In this section, we give a step-by-step demo of using Pi to carry out disease-specific genetic prioritisation of targets at the gene and pathway level, focusing on three inflammatory diseases as exemplars: Ankylosing Spondylitis (AS), Spondyloarthritis (SA, including AS and Psoriatic Arthritis), and Systemic Lupus Erythematosus (SLE).

First of all, load the packages including Pi:

library(Pi)
# Following packages are also needed
library(XGR)
library(RCircos)
library(VennDiagram)

# optionally, specify the local location of built-in RData
# by default:
RData.location <- "https://github.com/hfang-bristol/RDataCentre/blob/master/Portal"

5.1 Ankylosing Spondylitis

GWAS SNPs are collected mainly from GWAS Catalog (Welter et al. 2014), complemented by ImmunoBase and latest publications.

Import AS-associated GWAS lead SNPs (with the help of Anna Sanniti)

data.file <- "http://galahad.well.ox.ac.uk/bigdata/AS.txt"
AS <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)

The first 5 rows of the data frame AS are shown below, with the column SNP for AS GWAS SNPs and the column Pval for GWAS-detected P-values.

SNP	Pval
rs10045403	5.8e-14
rs1018326	2.0e-06
rs10440635	3.0e-07
rs10781500	1.0e-06
rs10865331	1.9e-19

5.1.1 Gene-level prioritisation

It includes the following steps:

Define seed genes based on distance-to-SNP window and genetic association with gene expression: nearby genes that are located within 200kb distance window of lead or Linkage Disequilibrium (LD) SNPs that are calculated based on European population data from 1000 Genome Project; expression associated genes (eQTL genes) for which gene expression is, either in a cis- or trans-acting manner, significantly associated with lead or LD SNPs, based on immune-stimulated eQTL data in monocytes (Fairfax et al. 2014).
Score seed genes to quantify the genetic influence under lead or LD SNPs.
Prioritise target genes by estimating their global network connectivity to seed genes. With scored seed genes superposed onto a gene interaction network (see above STRING_high), RWR algorithm is implemented to prioritise candidate target genes based on their network connectivity/affinity to seed genes. As such, a gene that has a higher affinity score to seed genes will receive a higher priority score.

pAS <- xPrioritiserSNPs(data=AS, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, decay.kernel="rapid", restart=0.75, RData.location=RData.location)

The results are stored in the data frame pAS$priority, which can be saved into a file AS.genes_priority.txt:

AS_genes <- pAS$priority
write.table(AS_genes, file="AS.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
CAST	1	52.13154	0.08460	1
ERAP2	1	22.33534	0.03626	2
ERAP1	1	20.02777	0.03276	3
B3GNT2	1	12.76706	0.02077	4
IL23R	1	11.19898	0.01823	5
LNPEP	1	10.36132	0.01696	6
DDX39B	1	7.91010	0.01284	7
IL12RB2	1	7.32161	0.01197	8
ETS2	1	7.14037	0.01160	9
PSMG1	1	6.69782	0.01091	10
TBKBP1	1	6.58648	0.01071	11
NFKBIL1	1	6.00768	0.01039	12
KIF21B	1	5.82870	0.01008	13
GPR65	1	5.97141	0.00969	14
C1orf106	1	5.21012	0.00854	15
IL6R	1	4.57166	0.00747	16
TNFRSF1A	1	4.24279	0.00717	17
NPEPPS	1	4.12215	0.00670	18
KPNB1	1	4.09678	0.00666	19
GALC	1	4.01292	0.00654	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_AS <- xPrioritiserManhattan(pAS, highlight.top=20, cex=0.5, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_AS)

5.1.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pAS, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `AS.pathways_priority.txt`
AS_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(AS_pathways), AS_pathways)
write.table(output, file="AS.pathways_priority.txt", sep="\t", row.names=F)

The top 5 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
IL27-mediated signaling events	26	5	11.00	2.7e-08	6.3e-07	IL12B, TYK2, IL12RB2, TBX21, IL27
IL12-mediated signaling events	63	7	9.63	1.6e-08	6.3e-07	NFKB1, IL12B, TYK2, IL12RB2, IL2RA, NOS2, TBX21
NO2-dependent IL 12 Pathway in NK cells	17	4	11.00	1.1e-07	1.7e-06	IL12B, TYK2, IL12RB2, NOS2
IL23-mediated signaling events	37	5	9.08	2.5e-07	3.0e-06	NFKB1, IL12B, TYK2, NOS2, IL23R
Cytokine-cytokine receptor interaction	266	11	6.55	4.6e-07	4.3e-06	CSF2RB, TNFRSF1A, IL12B, IL6R, IL12RB2, IL2RA, CXCR2, LTBR, IL23R, IL1R2, CD27

This barplot displays the top 5 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- AS_pathways[1:5,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pAS, priority.top=100, ontology="SF", RData.location=RData.location)
AS_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 8 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Leukotriene A4 hydrolase N-terminal domain	12	4	15.30	3.6e-09	4.3e-08	LNPEP, ERAP2, NPEPPS, ERAP1
TNF receptor-like	24	3	7.87	8.6e-06	5.2e-05	TNFRSF1A, CD27, LTBR
p53-like transcription factors	43	3	5.67	9.2e-05	3.7e-04	NFKB1, RUNX3, TBX21
Metalloproteases (zincins), catalytic domain	100	4	4.65	2.3e-04	6.3e-04	LNPEP, ERAP2, NPEPPS, ERAP1
Histone-fold	107	4	4.44	3.2e-04	6.3e-04	HIST1H4I, HIST1H3A, HIST1H3C, HIST1H3B
Immunoglobulin	481	9	3.95	3.1e-04	6.3e-04	TAPBPL, IL1R2, ICAM3, ICAM4, FCGR2A, FCGR2B, FCGR3A, IL6R, IL12B
Fibronectin type III	200	5	3.72	8.5e-04	1.5e-03	IL6R, IL12B, IL12RB2, CSF2RB, IL23R
DEATH domain	87	3	3.64	1.4e-03	2.1e-03	NFKB1, CARD9, TNFRSF1A

GO molecular function

eTerm <- xPrioritiserPathways(pNode=pAS, priority.top=100, ontology="GOMF", RData.location=RData.location)
AS_gomf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Leukotriene A4 hydrolase N-terminal domain	12	4	15.300	3.6e-09	4.3e-08	LNPEP, ERAP2, NPEPPS, ERAP1
TNF receptor-like	24	3	7.870	8.6e-06	5.2e-05	TNFRSF1A, CD27, LTBR
p53-like transcription factors	43	3	5.670	9.2e-05	3.7e-04	NFKB1, RUNX3, TBX21
Metalloproteases (zincins), catalytic domain	100	4	4.650	2.3e-04	6.3e-04	LNPEP, ERAP2, NPEPPS, ERAP1
Histone-fold	107	4	4.440	3.2e-04	6.3e-04	HIST1H4I, HIST1H3A, HIST1H3C, HIST1H3B
Immunoglobulin	481	9	3.950	3.1e-04	6.3e-04	TAPBPL, IL1R2, ICAM3, ICAM4, FCGR2A, FCGR2B, FCGR3A, IL6R, IL12B
Fibronectin type III	200	5	3.720	8.5e-04	1.5e-03	IL6R, IL12B, IL12RB2, CSF2RB, IL23R
DEATH domain	87	3	3.640	1.4e-03	2.1e-03	NFKB1, CARD9, TNFRSF1A
Homeodomain-like	294	3	1.080	8.1e-02	1.1e-01	NKX2-3, MIER1, SNAPC4
PH domain-like	420	3	0.442	2.1e-01	2.5e-01	TYK2, PLEKHG6, SH2B3

5.1.3 Network-level prioritisation

Network-level prioritisation is to identify a gene network that contains as many highly prioritised genes as possible but a few lowly prioritised genes as linkers. Given a gene network (the same as used in gene-level prioritisation) with nodes labelled with gene priority scores, the search for a maximum-scoring gene subnetwork is briefed as follows:

score transformation, that is, given a threshold of tolerable priority scores, nodes above this threshold (nodes of interest) are scored positively, and negative scores for nodes below the threshold (intolerable).
subnetwork identification, that is, to find an interconnected gene subnetwork enriched with positive-score nodes but allowing for a few negative-score nodes as linkers, done via heuristically solving prize-collecting Steiner tree problem (Fang and Gough 2014).
controlling the subnetwork size, that is, an iterative procedure of scanning different priority thresholds is used to identify the network with a desired number of nodes/genes.

Notably, the preferential use of the same network as used in gene-level prioritisation is due to the fact that gene-level affinity/priority scores are smoothly distributed over the network after being walked. In other words, the chance of identifying such a gene network enriched with top prioritised genes is much higher. To reduce the runtime, by default only top 10% prioritised genes will be used to search for the maximum-scoring gene network.

# find maximum-scoring gene network with the desired node number=50
AS_net <- xPrioritiserSubnet(pNode=pAS, priority.quantite=0.1, subnet.size=50, RData.location=RData.location)

The identified gene network has nodes/genes colored according to their priority scores (see below). Notably, if nodes appear abnormally, please remove vertex.shape="sphere" when running the function xVisNet.

g <- AS_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
IL12-mediated signaling events	63	11	19.10	1.1e-16	6.7e-15	NFKB1, LCK, B2M, IL12B, SOCS1, TYK2, IL12RB2, IL1R1, IL2RA, NOS2, TBX21
IL27-mediated signaling events	26	5	13.50	2.4e-09	7.0e-08	IL12B, TYK2, IL12RB2, TBX21, IL27
Cytokine-cytokine receptor interaction	266	11	8.52	4.2e-09	7.0e-08	CSF2RB, TNFRSF1A, IL12B, IL6R, IL12RB2, IL1R1, IL2RA, CXCR1, CXCR2, IL23R, IL1R2
Cytokine Signaling in Immune system	268	11	8.48	4.6e-09	7.0e-08	CSF2RB, ICAM1, LCK, B2M, SOCS1, IL6R, TYK2, IL1R1, IL2RA, IL1R2, UBA52
NO2-dependent IL 12 Pathway in NK cells	17	4	13.50	1.5e-08	1.8e-07	IL12B, TYK2, IL12RB2, NOS2
IL23-mediated signaling events	37	5	11.20	2.3e-08	2.4e-07	NFKB1, IL12B, TYK2, NOS2, IL23R
Signaling by Interleukins	106	7	8.95	4.3e-08	3.8e-07	CSF2RB, LCK, IL6R, TYK2, IL1R1, IL2RA, IL1R2
Jak-STAT signaling pathway	155	8	8.28	5.6e-08	4.2e-07	CSF2RB, IL12B, SOCS1, IL6R, TYK2, IL12RB2, IL2RA, IL23R
Hematopoietic cell lineage	87	6	8.48	2.1e-07	1.4e-06	ITGB3, IL6R, IL1R1, IL2RA, IL1R2, CD9
Immune System	912	16	5.66	7.6e-07	4.6e-06	CSF2RB, ICAM1, LCK, B2M, SOCS1, IL6R, TYK2, IL1R1, IL2RA, ICAM3, IL1R2, LNPEP, UBA52, ICAM4, ERAP1, NPEPPS

Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 5 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Neutral zinc metallopeptidase	182	4	3.56	0.00120	0.0033	ERAP2, LNPEP, NPEPPS, ERAP1
Cell surface	467	7	3.50	0.00081	0.0033	TNFRSF1A, B2M, SCNN1A, ICAM1, CD9, IL1R1, CXCR2
External side of plasma membrane	189	4	3.46	0.00140	0.0033	B2M, SCNN1A, ICAM1, CD9
Drug resistance	349	5	2.80	0.00410	0.0071	B2M, ICAM1, SOCS1, LCK, PDE4A
Protease	622	5	1.37	0.05600	0.0790	ERAP2, LNPEP, NPEPPS, ERAP1, CAPNS1

5.2 Spondyloarthritis

Import SA-associated GWAS lead SNPs (with the help of Anna Sanniti and Alicia Lledo Lara)

data.file <- "http://galahad.well.ox.ac.uk/bigdata/Spondyloarthritis.txt"
SA <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)

5.2.1 Gene-level prioritisation

pSA <- xPrioritiserSNPs(data=SA, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, decay.kernel="rapid", restart=0.75, RData.location=RData.location)

The results are stored in the data frame pSA$priority, which can be saved into a file SA.genes_priority.txt:

SA_genes <- pSA$priority
write.table(SA_genes, file="SA.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
CAST	1	52.13154	0.02679	1
DDX39B	1	43.38974	0.02230	2
ATF6B	1	37.30371	0.01918	3
NFKBIL1	1	33.00396	0.01806	4
IER3	1	22.88007	0.01174	5
ERAP2	1	22.33534	0.01148	6
ERAP1	1	20.02777	0.01037	7
TRAF3IP2	1	20.03787	0.01030	8
MDC1	1	19.39800	0.00998	9
TNIP1	1	18.67828	0.00967	10
ANXA6	1	17.98198	0.00925	11
VARS2	1	17.75845	0.00913	12
LCE3D	1	14.03913	0.00903	13
LCE3A	1	13.93991	0.00849	14
LCE3B	1	13.99650	0.00818	15
LCE3C	1	12.22289	0.00802	16
LCE3E	1	12.45889	0.00751	17
TYK2	1	12.86998	0.00672	18
B3GNT2	1	12.76706	0.00657	19
CNPY2	1	12.65046	0.00652	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_SA <- xPrioritiserManhattan(pSA, highlight.top=20, cex=0.4, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_SA)

5.2.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pSA, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `SA.pathways_priority.txt`
SA_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(SA_pathways), SA_pathways)
write.table(output, file="SA.pathways_priority.txt", sep="\t", row.names=F)

The top 10 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
NO2-dependent IL 12 Pathway in NK cells	17	4	12.30	3.5e-08	1.0e-06	IL12B, TYK2, IL12RB2, NOS2
IL23-mediated signaling events	37	5	10.30	6.5e-08	1.0e-06	IL12B, TYK2, NOS2, IL23A, IL23R
IL27-mediated signaling events	26	4	9.84	3.6e-07	3.8e-06	STAT2, IL12B, TYK2, IL12RB2
IL12 and Stat4 Dependent Signaling Pathway in Th1 Development	23	3	7.79	9.0e-06	7.2e-05	IL12B, TYK2, IL12RB2
Beta2 integrin cell surface interactions	29	3	6.85	2.3e-05	1.5e-04	ICAM1, ICAM3, ICAM4
IL12-mediated signaling events	63	4	5.97	3.3e-05	1.5e-04	IL12B, TYK2, IL12RB2, NOS2
Jak-STAT signaling pathway	155	6	5.36	3.2e-05	1.5e-04	STAT2, IL12B, TYK2, IL12RB2, IL23A, IL23R
Allograft rejection	37	3	5.97	6.3e-05	2.2e-04	IL12B, HLA-E, HLA-DQA1
Viral myocarditis	71	4	5.55	5.8e-05	2.2e-04	FYN, ICAM1, HLA-E, HLA-DQA1
Type I diabetes mellitus	43	3	5.47	1.1e-04	3.7e-04	IL12B, HLA-E, HLA-DQA1

This barplot displays the top 10 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- SA_pathways[1:10,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pSA, priority.top=100, ontology="SF", RData.location=RData.location)
SA_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 6 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Leukotriene A4 hydrolase N-terminal domain	12	3	12.50	2.0e-07	1.8e-06	LNPEP, ERAP2, ERAP1
Metalloproteases (zincins), catalytic domain	100	3	3.75	1.2e-03	4.0e-03	LNPEP, ERAP2, ERAP1
SH2 domain	112	3	3.46	1.8e-03	4.0e-03	TYK2, STAT2, FYN
Immunoglobulin	481	7	3.26	1.6e-03	4.0e-03	HLA-DQA1, HLA-E, ICAM1, ICAM3, ICAM4, ICAM5, IL12B
Fibronectin type III	200	3	2.17	1.4e-02	2.5e-02	IL12B, IL12RB2, IL23R
WD40 repeat-like	281	3	1.51	4.1e-02	5.8e-02	KIF21B, PAN2, WDR83

GO molecular function

eTerm <- xPrioritiserPathways(pNode=pSA, priority.top=100, ontology="GOMF", RData.location=RData.location)
SA_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
aminopeptidase activity	28	3	7.67	1.1e-05	0.00019	ERAP2, LNPEP, ERAP1
integrin binding	105	4	4.83	1.8e-04	0.00160	ICAM1, ICAM3, ICAM4, ICAM5
ATP-dependent RNA helicase activity	63	3	4.81	2.7e-04	0.00160	DHX30, SKIV2L, DDX39B
RNA binding	404	5	2.13	1.5e-02	0.07000	RPL34, SETD1A, ILF3, SKIV2L, PUS10
receptor binding	321	4	1.92	2.2e-02	0.08000	HLA-E, ICAM3, NOS2, APOF
signal transducer activity	227	3	1.77	2.7e-02	0.08100	STAT2, SLC44A2, KCNH7
poly(A) RNA binding	1118	9	1.51	5.1e-02	0.13000	MRPL4, RAVER1, DHX30, SSRP1, XRCC6, ILF3, CAST, DDX39B, CS
nucleotide binding	304	3	1.22	6.6e-02	0.15000	RAVER1, REV3L, SETD1A
protein serine/threonine kinase activity	325	3	1.09	8.0e-02	0.16000	SQSTM1, CSNK2A1, BCKDK
ATP binding	1454	10	1.07	1.1e-01	0.19000	FYN, CSNK2A1, DHX30, XRCC6, SKIV2L, KIF21B, BCKDK, VARS2, DDX39B, TYK2

5.2.3 Network-level prioritisation

# find maximum-scoring gene network with the desired node number=50
SA_net <- xPrioritiserSubnet(pNode=pSA, priority.quantite=0.1, subnet.size=50, RData.location=RData.location)

g <- SA_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Beta2 integrin cell surface interactions	29	7	17.10	2.0e-12	1.9e-10	ICAM1, ITGAL, ITGAX, ITGAM, ICAM3, FCGR2A, ICAM4
Integrin cell surface interactions	79	8	11.50	3.8e-10	1.4e-08	ITGB1, ICAM1, ITGAL, ITGAX, ITGB3, ITGAM, ICAM3, ICAM4
Jak-STAT signaling pathway	155	10	9.93	6.0e-10	1.4e-08	IL4, STAT2, IL12B, IL13, IL6R, TYK2, IL12RB2, CTF1, IL23A, IL23R
Immune System	912	21	7.46	5.9e-10	1.4e-08	NFKBIA, ITGB1, RPS6KA1, STAT2, FYN, ICAM1, ITGAL, TNFAIP3, IL6R, TYK2, PSMA6, ICAM3, DDX58, RNF41, LNPEP, UBA52, REL, SQSTM1, ICAM4, ERAP1, NPEPPS
IL23-mediated signaling events	37	6	12.80	9.9e-10	1.9e-08	NFKBIA, IL12B, TYK2, NOS2, IL23A, IL23R
Monocyte and its Surface Molecules	11	4	15.90	2.0e-09	3.1e-08	ITGB1, ICAM1, ITGAL, ITGAM
IL27-mediated signaling events	26	5	12.80	4.7e-09	5.0e-08	STAT2, IL12B, TYK2, IL12RB2, TBX21
Integrin family cell surface interactions	26	5	12.80	4.7e-09	5.0e-08	ITGB1, ITGAL, ITGAX, ITGB3, ITGAM
Leishmania infection	72	7	10.50	4.8e-09	5.0e-08	NFKBIA, IL4, ITGB1, IL12B, ITGAM, NOS2, FCGR2A
NO2-dependent IL 12 Pathway in NK cells	17	4	12.70	2.6e-08	2.4e-07	IL12B, TYK2, IL12RB2, NOS2

Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 5 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
External side of plasma membrane	189	7	6.75	1.2e-06	1.2e-05	SCNN1A, ITGAL, ITGAX, ITGB1, ICAM1, IL4, IL13
Cell surface	467	9	4.93	2.1e-05	1.1e-04	TNFRSF1A, SCNN1A, STX4, ITGAL, ITGAX, ITGB1, ICAM1, IL4, IL13
Tumor suppressor	716	9	3.40	7.7e-04	2.6e-03	ITGB1, TNFAIP3, UBA52, IL12B, CSNK2A1, PSMA6, ETS1, CDC37, RUNX3
Protease inhibitor	179	3	2.47	8.1e-03	1.7e-02	TNFAIP3, RPS6KA1, CAST
Neutral zinc metallopeptidase	182	3	2.44	8.6e-03	1.7e-02	LNPEP, NPEPPS, ERAP1

5.3 Systemic Lupus Erythematosus

Import SLE-associated GWAS lead SNPs

data.file <- "http://galahad.well.ox.ac.uk/bigdata/SLE.txt"
SLE <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)

5.3.1 Gene-level prioritisation

pSLE <- xPrioritiserSNPs(data=SLE, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, decay.kernel="rapid", restart=0.75, RData.location=RData.location)

The results are stored in the data frame pSLE$priority, which can be saved into a file SLE.genes_priority.txt:

SLE_genes <- pSLE$priority
write.table(SLE_genes, file="SLE.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
ATF6B	1	149.00804	0.04358	1
NELFE	1	79.37208	0.02335	2
VARS2	1	69.41208	0.02029	3
ITGAM	1	63.64914	0.01884	4
PBX2	1	60.75875	0.01783	5
PYCARD	1	59.68441	0.01753	6
CUEDC2	1	59.78891	0.01746	7
STAT4	1	57.81733	0.01707	8
HLA-DPB1	1	52.92738	0.01576	9
IRF5	1	52.36363	0.01540	10
TNPO3	1	52.36363	0.01533	11
CLIC1	1	52.07534	0.01527	12
PSMB9	1	50.63699	0.01482	13
RNF114	1	48.59792	0.01427	14
C2	1	46.16449	0.01354	15
LTA	1	42.43087	0.01303	16
SKIV2L	1	42.63385	0.01275	17
NCF2	1	41.58059	0.01219	18
HNRNPM	1	41.47880	0.01217	19
SMG7	1	38.82899	0.01137	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_SLE <- xPrioritiserManhattan(pSLE, highlight.top=20, cex=0.4, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_SLE)

5.3.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pSLE, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `SLE.pathways_priority.txt`
SLE_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(SLE_pathways), SLE_pathways)
write.table(output, file="SLE.pathways_priority.txt", sep="\t", row.names=F)

The top 10 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Beta2 integrin cell surface interactions	29	5	10.50	5.0e-08	1.4e-06	ITGAX, ITGAM, ICAM3, FCGR2A, ITGAD
Leishmania infection	72	7	9.00	4.3e-08	1.4e-06	STAT1, NCF2, ITGAM, HLA-DOB, HLA-DPB1, HLA-DQA1, FCGR2A
Immune System	912	21	5.90	2.1e-07	3.9e-06	CD80, STAT1, TNFAIP3, C2, C4A, CFB, NCF2, TYK2, SKP1, UBE2L3, HLA-DOB, HLA-DPB1, HLA-DQA1, ICAM3, PYCARD, PSMB9, IFIH1, IRF5, BLK, IRF8, SQSTM1
Type I diabetes mellitus	43	5	8.42	5.9e-07	8.4e-06	CD80, LTA, HLA-DOB, HLA-DPB1, HLA-DQA1
Initial triggering of complement	13	3	9.47	1.8e-06	1.7e-05	C2, C4A, CFB
Regulation of Complement cascade	13	3	9.47	1.8e-06	1.7e-05	C2, C4A, CFB
IL22 Soluble Receptor Signaling Pathway	16	3	8.48	4.4e-06	3.6e-05	STAT1, STAT4, TYK2
Interferon gamma signaling	62	5	6.81	5.3e-06	3.8e-05	STAT1, HLA-DPB1, HLA-DQA1, IRF5, IRF8
Allograft rejection	37	4	7.22	6.4e-06	3.9e-05	CD80, HLA-DOB, HLA-DPB1, HLA-DQA1
Systemic lupus erythematosus	139	7	6.01	6.8e-06	3.9e-05	CD80, C2, C4A, HLA-DOB, HLA-DPB1, HLA-DQA1, FCGR2A

This barplot displays the top 10 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- SLE_pathways[1:10,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pSLE, priority.top=100, ontology="SF", RData.location=RData.location)
SLE_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 8 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
vWA-like	95	6	7.33	1.3e-06	2.0e-05	CFB, C2, ITGAD, ITGAM, ITGAX, XRCC6
Integrin alpha N-terminal domain	23	3	7.82	8.9e-06	6.3e-05	ITGAD, ITGAM, ITGAX
Integrin domains	25	3	7.47	1.3e-05	6.3e-05	ITGAD, ITGAM, ITGAX
MHC antigen-recognition domain	40	3	5.73	8.6e-05	3.2e-04	HLA-DOB, HLA-DPB1, HLA-DQA1
TNF-like	53	3	4.84	2.6e-04	7.8e-04	LTB, TNFSF4, LTA
SH2 domain	112	4	4.15	5.0e-04	1.3e-03	TYK2, STAT1, STAT4, BLK
RING/U-box	334	5	2.20	1.4e-02	2.9e-02	SQSTM1, TRIM72, PPIL2, RNF114, PHRF1
Immunoglobulin	481	6	1.93	2.3e-02	3.8e-02	HLA-DOB, HLA-DPB1, HLA-DQA1, ICAM3, CD80, FCGR2A

GO molecular function

eTerm <- xPrioritiserPathways(pNode=pSLE, priority.top=100, ontology="GOMF", RData.location=RData.location)
SLE_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
tumor necrosis factor receptor binding	30	4	9.30	6.7e-07	1.5e-05	STAT1, LTA, LTB, TNFSF4
ATP-dependent RNA helicase activity	63	3	4.43	4.5e-04	5.2e-03	DHX30, SKIV2L, DDX39B
receptor binding	321	6	3.13	2.3e-03	1.8e-02	HSPA1A, ICAM3, LTA, BLK, LTB, TNFSF4
actin filament binding	113	3	2.96	3.9e-03	2.3e-02	AIF1, ARPC5, FLNC
sequence-specific DNA binding transcription factor activity	825	10	2.53	6.9e-03	3.2e-02	IKZF1, PTTG1, MECP2, ETS1, ATF6B, PBX2, PHTF1, STAT1, STAT4, IRF5
cytokine activity	172	3	2.06	1.7e-02	5.9e-02	LTA, LTB, TNFSF4
transcription factor binding	266	4	2.04	1.8e-02	5.9e-02	MECP2, ETS1, PBX2, GPX3
ligase activity	283	4	1.91	2.3e-02	6.5e-02	RNF114, UBE2L3, PPIL2, TNFAIP3
sequence-specific DNA binding	407	5	1.79	2.8e-02	6.6e-02	IKZF1, ETS1, ATF6B, PBX2, STAT4
ATP binding	1454	13	1.74	3.4e-02	6.6e-02	UBE2L3, IFIH1, HSPA1A, DHX30, BLK, XRCC6, SKIV2L, RENBP, VARS2, DDX39B, NADSYN1, NMNAT2, TYK2

5.3.3 Network-level prioritisation

# find maximum-scoring gene network with the desired node number=50
SLE_net <- xPrioritiserSubnet(pNode=pSLE, priority.quantite=0.1, subnet.size=50, RData.location=RData.location)

g <- SLE_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Immune System	912	22	8.63	4.5e-12	4.9e-10	CD80, HRAS, STAT1, FCGR2B, CSK, CDKN1B, NCF2, SOCS1, IRAK1, TYK2, CD44, SKP1, RASGRP3, IRF7, PYCARD, FCGR3A, RASGRP1, UBA52, IRF5, BLK, IRF8, SQSTM1
Leishmania infection	72	8	12.80	5.7e-11	3.1e-09	IL10, STAT1, TNF, NCF2, ITGAM, IRAK1, FCGR2A, FCGR3A
Cytokine Signaling in Immune system	268	12	9.36	3.4e-10	1.2e-08	HRAS, STAT1, SOCS1, IRAK1, TYK2, CD44, SKP1, IRF7, UBA52, IRF5, IRF8, SQSTM1
IL-10 Anti-inflammatory Signaling Pathway	17	4	13.50	1.5e-08	4.1e-07	IL10, STAT1, STAT4, TNF
Interferon gamma signaling	62	6	10.30	1.9e-08	4.3e-07	STAT1, SOCS1, CD44, IRF7, IRF5, IRF8
Interferon alpha/beta signaling	64	6	10.10	2.4e-08	4.5e-07	STAT1, SOCS1, TYK2, IRF7, IRF5, IRF8
Interferon Signaling	158	8	8.19	6.6e-08	1.0e-06	STAT1, SOCS1, TYK2, CD44, IRF7, UBA52, IRF5, IRF8
IL27-mediated signaling events	26	4	10.80	1.5e-07	1.7e-06	STAT1, STAT4, TNF, TYK2
Signaling by the B Cell Receptor (BCR)	123	7	8.20	1.4e-07	1.7e-06	HRAS, CDKN1B, SKP1, RASGRP3, RASGRP1, UBA52, BLK
Adaptive Immune System	524	13	6.61	1.4e-07	1.7e-06	CD80, HRAS, FCGR2B, CSK, CDKN1B, NCF2, SOCS1, SKP1, RASGRP3, FCGR3A, RASGRP1, UBA52, BLK

Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 5 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Clinically actionable	237	6	4.30	0.00020	0.0017	CDKN1B, SOCS1, TP53, HRAS, PRDM1, STAT4
Histone modification	249	6	4.14	0.00028	0.0017	TP53, CDK9, HCFC1, KAT8, MECP2, SKP1
External side of plasma membrane	189	5	4.04	0.00042	0.0017	TNF, ITGAX, CD80, FCGR3A, CD44
Drug resistance	349	6	3.13	0.00210	0.0062	TNF, SOCS1, TP53, IL10, GPX3, NCF2
Tumor suppressor	716	9	2.82	0.00320	0.0076	TNF, CDKN1B, TP53, HRAS, CDK9, UBA52, HCFC1, ETS1, CDC37

5.4 Dupuytren’s disease (DD)

Import DD-associated GWAS lead SNPs

data.file <- "http://galahad.well.ox.ac.uk/bigdata/Dupuytren.txt"
DD <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)

The 13 DD-associated SNPs are:

SNPs	Pvalue
rs16879765	6e-39
rs6519955	3e-33
rs611744	8e-15
rs11672517	7e-14
rs2912522	2e-13
rs8124695	8e-10
rs7524102	3e-09
rs10809650	6e-09
rs4730775	3e-08
rs2179367	3e-07
rs4789939	6e-07
rs4932194	8e-07
rs12032381	6e-06

5.4.1 Gene-level prioritisation

pDD <- xPrioritiserSNPs(data=DD, include.LD="EUR", include.eQTL=c("JKscience_TS2B","JKscience_TS3A"), network="STRING_high", significance.threshold=5e-5, distance.max=200000, decay.kernel="rapid", restart=0.75, RData.location=RData.location)

The results are stored in the data frame pDD$priority, which can be saved into a file DD.genes_priority.txt:

DD_genes <- pDD$priority
write.table(DD_genes, file="DD.genes_priority.txt", sep="\t", row.names=F)

The top 20 genes are listed below:

name	seed	weight	priority	rank
PRR34	1	11.96220	0.11660	1
SFRP4	1	12.43433	0.11429	2
NME8	1	10.15061	0.09302	3
WNT7B	1	8.90219	0.08214	4
EIF3E	1	4.89795	0.04480	5
DUXA	1	4.42695	0.04138	6
RSPO2	1	4.50962	0.04136	7
ZIM3	1	3.52149	0.03572	8
USP29	1	3.01678	0.03165	9
BPIFC	0	0.00000	0.02915	10
PPARA	1	2.79592	0.02560	11
TIMP2	1	2.23830	0.02052	12
AURKC	1	2.04431	0.01872	13
WNT2	1	1.61094	0.01569	14
ZC3H12D	1	1.04283	0.00953	15
NUP43	1	0.96004	0.00878	16
ZBTB40	1	0.91339	0.00849	17
PCMT1	1	0.91664	0.00838	18
ZNF460	1	0.82780	0.00758	19
TAB2	1	0.80465	0.00737	20

These top 20 genes are also highlighted in manhattan plot, in which priority scores for genes are displayed on the Y-axis along with genomic locations on the X-axis.

mp_DD <- xPrioritiserManhattan(pDD, highlight.top=20, cex=0.4, highlight.col="red", highlight.label.size=3, RData.location=RData.location)
print(mp_DD)

5.4.2 Pathway-level prioritisation

Pathway-level prioritisation is based on top 100 genes using a compendium of pathways from diverse sources (Canonical, KEGG, BioCarta and Reactome).

eTerm <- xPrioritiserPathways(pNode=pDD, priority.top=100, ontology="MsigdbC2CPall", RData.location=RData.location)
# view the top pathways/terms
xEnrichViewer(eTerm)
# save results to a file `DD.pathways_priority.txt`
DD_pathways <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)
output <- data.frame(term=rownames(DD_pathways), DD_pathways)
write.table(output, file="DD.pathways_priority.txt", sep="\t", row.names=F)

The top 10 prioritised pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Basal cell carcinoma	55	29	47.4	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, FZD9, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B
Melanogenesis	101	29	34.7	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, FZD9, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B
Wnt signaling pathway	151	32	31.1	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, FZD9, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B, SFRP4, DKK2, DKK1
Class B/2 (Secretin family receptors)	87	27	34.8	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, FZD9, WNT3A, FZD10, WNT16, FZD5, FZD3
Genes related to Wnt-mediated signal transduction	89	26	33.1	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT11, WNT2B, FZD4, WNT9A, FZD6, FZD7, FZD2, FZD8, WNT10A, WNT3A, WNT16, FZD5, FZD3, WNT5B, SFRP4, DKK1
Wnt signaling network	28	17	38.9	0.0e+00	0.0e+00	WNT1, FZD1, WNT2, WNT3, WNT5A, WNT7A, WNT7B, FZD4, FZD6, FZD7, FZD2, FZD8, FZD9, WNT3A, FZD10, FZD5, DKK1
Hedgehog signaling pathway	56	19	30.5	0.0e+00	0.0e+00	WNT1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, WNT9A, WNT9B, WNT10A, WNT3A, WNT16, WNT5B
Pathways in cancer	325	29	18.5	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, FZD9, WNT3A, FZD10, WNT16, FZD5, FZD3, WNT5B
GPCR ligand binding	400	27	15.2	0.0e+00	0.0e+00	WNT1, FZD1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8B, WNT10B, WNT11, WNT2B, FZD4, WNT9A, FZD6, WNT9B, FZD7, FZD2, FZD8, WNT10A, FZD9, WNT3A, FZD10, WNT16, FZD5, FZD3
Genes encoding secreted soluble factors	344	21	12.6	1.4e-16	3.2e-16	WNT1, WNT4, WNT2, WNT3, WNT5A, WNT6, WNT7A, WNT7B, WNT8A, WNT8B, WNT10B, WNT11, WNT2B, WNT9A, WNT9B, WNT10A, WNT3A, WNT16, WNT5B, SFRP4, MEGF6

This barplot displays the top 10 prioritised pathways/terms, where a vertical line indicates the adjusted p-value cutoff at 0.05.

df <- DD_pathways[1:10,]
df$name <- factor(df$name, levels=df$name)
p <- ggplot(df, aes(x=name, y=-1*log10(adjp)))
p + geom_bar(stat="identity", fill="deepskyblue") + ylab("Priority scores: -log10(adjp)") + theme(axis.title.y=element_blank(), axis.text.y=element_text(size=12,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=-log10(0.05),color="blue") + coord_flip()

Similarly, look at enrichments for top genes from different aspects:

SCOP domain superfamilies

eTerm <- xPrioritiserPathways(pNode=pDD, priority.top=100, ontology="SF", RData.location=RData.location)
DD_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 6 significant SCOP domains are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Frizzled cysteine-rich domain	20	11	34.00	0.0e+00	0.0e+00	SFRP4, FZD1, FZD4, FZD6, FZD7, FZD8, FZD10, FZD9, FZD3, FZD5, FZD2
Thioredoxin-like	127	12	14.10	6.8e-14	3.1e-13	TXNL1, TXNRD1, TXNRD3, PRDX2, PRDX1, NME8, PRDX3, TXNDC2, PRDX5, TXNDC8, TXN2, PRDX4
FAD/NAD(P)-binding domain	54	3	5.17	1.7e-04	5.1e-04	TXNRD1, TXNRD3, TXNRD2
Growth factor receptor domain	127	4	4.15	5.0e-04	1.1e-03	RSPO4, MEGF6, RSPO3, RSPO2
Cysteine proteinases	149	3	2.56	7.4e-03	1.3e-02	USP5, USP29, USP36
Homeodomain-like	294	4	2.04	1.8e-02	2.7e-02	DUXA, ARGFX, TPRX1, LEUTX

GO molecular function

eTerm <- xPrioritiserPathways(pNode=pDD, priority.top=100, ontology="GOMF", RData.location=RData.location)
DD_sf <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

The top 10 significant GO molecular function terms are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
frizzled binding	37	23	48.10	0.0e+00	0.0e+00	WNT5A, ZNRF3, WNT3A, WNT1, WNT3, RSPO3, WNT7A, WNT5B, FZD1, FZD7, WNT4, WNT11, WNT2, WNT8A, WNT10B, WNT6, WNT7B, WNT8B, WNT10A, WNT2B, WNT9A, WNT9B, WNT16
Wnt-activated receptor activity	21	11	30.50	0.0e+00	0.0e+00	FZD4, FZD1, FZD8, SFRP4, FZD5, FZD2, FZD6, FZD7, FZD9, FZD3, FZD10
Wnt-protein binding	28	11	26.30	4.0e-20	3.3e-19	FZD4, FZD1, FZD8, SFRP4, FZD5, FZD2, FZD6, FZD7, FZD9, FZD3, FZD10
receptor agonist activity	15	9	29.50	1.3e-19	8.8e-19	WNT5A, WNT3A, WNT1, WNT3, WNT7A, WNT4, WNT2, WNT8A, WNT10B
protein disulfide oxidoreductase activity	23	6	15.70	5.6e-11	3.2e-10	TXNRD1, TXNRD3, TXN2, TXNDC2, TXNDC8, TXNL1
PDZ domain binding	86	6	7.60	9.1e-07	4.2e-06	FZD4, FZD1, FZD8, FZD2, FZD7, FZD3
G-protein coupled receptor activity	643	12	4.18	1.4e-04	5.4e-04	FZD4, FZD1, FZD8, SFRP4, FZD5, FZD2, FZD6, FZD7, FZD9, FZD3, FZD10, LGR6
G-protein coupled receptor binding	45	3	5.22	1.6e-04	5.6e-04	RSPO3, RSPO2, RSPO4
flavin adenine dinucleotide binding	62	3	4.28	5.5e-04	1.7e-03	TXNRD1, TXNRD3, TXNRD2
microtubule motor activity	68	3	4.03	7.9e-04	2.0e-03	DNAH11, DNAH5, DNAI2

5.4.3 Network-level prioritisation

# find maximum-scoring gene network with the desired node number=20
DD_net <- xPrioritiserSubnet(pNode=pDD, priority.quantite=0.1, subnet.size=21, RData.location=RData.location)

g <- DD_net
pattern <- as.numeric(V(g)$priority)
zmax <- ceiling(quantile(pattern,0.75)*1000)/1000
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="yr", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Circos plot of the gene network is shown below:

# remove chromosomes without any genes
xCircos(g, entity="Gene", top_num=ecount(g), entity.label.cex=0.5, chr.exclude="auto", RData.location=RData.location)

Look at enrichments for genes in the network:

Pathways

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="MsigdbC2CPall", RData.location=RData.location)
g_path <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 10 significant pathways are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Basal cell carcinoma	55	9	25.80	3.5e-18	4.9e-17	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10
Class B/2 (Secretin family receptors)	87	9	20.40	4.7e-16	2.8e-15	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10
Genes related to Wnt-mediated signal transduction	89	9	20.20	5.9e-16	2.8e-15	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, SFRP4
Melanogenesis	101	9	18.90	2.2e-15	6.2e-15	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10
Wnt signaling pathway	151	10	17.10	1.8e-15	6.2e-15	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10, SFRP4
Wnt signaling network	28	6	24.20	7.4e-14	1.7e-13	WNT2, WNT7B, FZD4, FZD7, FZD8, FZD10
Pathways in cancer	325	11	12.50	2.2e-13	4.4e-13	EGFR, WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10, MMP9
Hedgehog signaling pathway	56	5	14.10	1.3e-09	2.3e-09	WNT2, WNT6, WNT7B, WNT11, WNT10A
GPCR ligand binding	400	9	8.95	2.2e-09	3.5e-09	WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10
Signaling by GPCR	906	10	6.06	4.7e-07	6.5e-07	EGFR, WNT2, WNT6, WNT7B, WNT11, FZD4, FZD7, FZD8, WNT10A, FZD10

Druggable categories

data <- V(g)$name
eTerm <- xEnricherGenes(data, ontology="DGIdb", RData.location=RData.location)
g_dbi <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp), sortBy="adjp", details=T)

Enrichment results for the top 3 significant druggable categories are shown below:

name	nAnno	nOverlap	zscore	pvalue	adjp	members
Cell surface	467	6	5.0	2.9e-05	5.8e-05	SFRP4, FZD10, WNT6, FZD4, RSPO2, TIMP2
G protein coupled receptor	855	4	1.6	3.4e-02	3.4e-02	FZD10, FZD4, FZD7, FZD8
NA	NA	NA	NA	NA	NA	NA

6 Ontology annotations & TPN nominations

In this section, we first describe annotation predictors using ontologies and target genes nominated by ULTRA-DD TPN members, and then integrate them as an additional support for disease-specific genetic prioritisation by Pi.

6.1 Annotation predictors

Using ontologies to annotate genes is one of the most effective ways to reuse existing knowledge. Also it is a scalable way to capture a particular knowledge sphere in an systematic way. An ontology is like a dictionary, containing vocabularies (called terms) and their relations. These terms and relations are understandable by humans, and readable by computers. According to how to organise relationships between terms, there are types of ontologies:

structured ontology: terms are organised in a tree-like structure, such as Gene Ontology (Ashburner et al. 2000), Disease Ontology (Schriml et al. 2012), and Phenotype Ontologies in human and mouse (Köhler et al. 2013; C. L. Smith and Eppig 2009);
non-structured ontology: terms are not organised as tree but simply listed as keywords, such as a collection of pathways, and gene druggable categories.

When integrating ontologies of different knowledge spheres, the use is limited by the heterogeneous nature: different terms/keywords represent the same or similar knowledge. For this reason, they are better used in a broader context to capture a general category of knowledge, eg a group of diseases instead of a specific disease. We consider the context broadly relevant to immune-related diseases. We choose 5 annotation predictors as informative carriers generalising their relatedness to immunity/inflammation diseases. For a given gene, we look at:

OMIM evidence: whether it was identified in OMIM as an immune disease ontology gene;
Phenotype evidence: whether it is annotated to immune-related phenotypes from Human Phenotype Ontology (HPO) and/or Mouse Phenotype Ontology (MPO);
Function evidence: whether it is annotated to immune/inflammatory terms from Gene Ontology;
Pathway evidence: whether it is annotated to immune-related pathways (from KEGG, BioCarta and Reactome);
Druggable evidence: whether it is annotated to gene druggable categories from DGIdb.

Since the above predictors are relatively independent to each other, additive scoring scheme is used to calculate annotation scores per gene.

Import annotation predictors (pre-computed)

data.file <- "http://galahad.well.ox.ac.uk/bigdata/iAnno.txt"
iAnno <- read.delim(data.file, header=TRUE, stringsAsFactors=FALSE)
# gene/target info
iSymbol <- iAnno[,2]
iGenes <- iAnno[,1:3]
# predictor info (including individual scores)
iPS <- iAnno[,22:26]
# additive predictor score
PS <- apply(iPS, 1, sum)
# combine additive predictor score and individual acores
iPS <- cbind(PS=PS, iPS)

The first 5 rows of the data frame iGenes are:

Target.GeneID	Target.Symbol	Target.Name
1	A1BG	alpha-1-B glycoprotein
2	A2M	alpha-2-macroglobulin
3	A2MP1	alpha-2-macroglobulin pseudogene 1
9	NAT1	N-acetyltransferase 1 (arylamine N-acetyltransferase)
10	NAT2	N-acetyltransferase 2 (arylamine N-acetyltransferase)

Their predictor info is stored in the data frame iPS, with the column PS for additive predictor scores:

PS	OMIM	Druggable
0	0	0
1	0	1
0	0	0
0	0	0
1	1	0

Distributions of annotation predictor scores

df <- iPS
p <- ggplot(df, aes(PS))
p + geom_bar(fill="deepskyblue") + scale_y_log10() + geom_text(stat="bin",binwidth=1,aes(label=..count..),vjust=0,color="blue") + ylab("Number of genes (predicted)") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + scale_x_continuous(breaks=0:max(df$PS)) + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue"))

Pairwise correlations between predictors

df <- df[df$PS>0, -1]
res <- supraHex::sDistance(t(df), metric="pearson")
diag(res) <- 1
cellnote <- signif(1-res, digits=2)
diag(cellnote) <- NA
visHeatmapAdv(1-res, Rowv=F, Colv=F, colormap="bwr", zlim=c(-1,1), key=F, cexRow=1, cexCol=1, cellnote=cellnote, notecex=0.8, notecol="black", lhei=c(1,5), lwid=c(1,5))

6.2 TPN nominations

# TPN nomination info
iTPN <- iAnno[,9:15]
# flag whether nominated by ULTRA-DD TPN members
iNominated <- iAnno[,9]
# info provided by SGC Oxford
iSGC <- iAnno[,4:8]
# iSGC restricted to nominated targets
df <- iSGC[iNominated>0, ]
df$SGC_PI <- !(df$SGC_PI %in% c("Unassigned","Unassigned - TO",""))

Gene family analysis

Below show results of family analysis (provided by Brian and David).

The top family is integral membrane proteins (IMP), followed by histone proteins (histone lysine demethylase; KDM) and ubiquitin-related proteins. Other top families include DENN domain proteins.

Allocations to SGC PIs

As seen below, 714 genes with 490 (68%) have been allocation to specific SGC-Oxford PIs.

SGC PI allocations are conditioned on SG/ChemBio Tractability:

Generally speaking, targets with good tractability are more likely to be allocated to SGC PIs.

Distributions of annotation predictor scores for nominated genes

Distributions of annotation predictor scores are conditioned on whether genes are nominated or not:

It can be seen that nominated genes tend to receive a higher annotation scores than non-nominated genes do.

6.3 Integration for Ankylosing Spondylitis

Recall: AS genes prioritised by Pi are stored in the data frame AS_genes, and the data frame iPS for annotation predictor scores for all genes stored in iSymbol or iGenes (see above).

# find genes with annotation predictors  
ind <- match(rownames(AS_genes), iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine AS_genes iPS iGenes iNominated
AS_combined <- cbind(AS_genes[,c(1,4)], genes_ap)

The top 20 AS-specific genes along with annotation predictors and TPN nominations are listed below:

name	priority	PS	OMIM	Phenotype	Function	Pathway	Druggable	Nominated
CAST	0.08460	1	0	0	0	0	1	0
ERAP2	0.03626	0	0	0	0	0	0	0
ERAP1	0.03276	2	0	0	1	1	0	0
B3GNT2	0.02077	0	0	0	0	0	0	0
IL23R	0.01823	3	1	1	1	0	0	0
LNPEP	0.01696	1	0	0	0	1	0	0
DDX39B	0.01284	1	0	0	0	0	1	0
IL12RB2	0.01197	1	0	1	0	0	0	0
ETS2	0.01160	0	0	0	0	0	0	0
PSMG1	0.01091	0	0	0	0	0	0	0
TBKBP1	0.01071	0	0	0	0	0	0	0
NFKBIL1	0.01039	1	1	0	0	0	0	0
KIF21B	0.01008	0	0	0	0	0	0	0
GPR65	0.00969	1	0	0	1	0	0	1
C1orf106	0.00854	0	0	0	0	0	0	0
IL6R	0.00747	5	1	1	1	1	1	0
TNFRSF1A	0.00717	4	1	1	1	0	1	1
NPEPPS	0.00670	2	0	0	0	1	1	0
KPNB1	0.00666	1	0	0	0	1	0	0
GALC	0.00654	1	0	1	0	0	0	0

Comparing genetic priority and annotation prediction

This violin plot displays density (the violin shape) of genes receiving AS-specific genetic priority versus annotation predictor scores (general indicator of immune-relatedness).

hline <- quantile(AS_combined$priority,0.95) # genetic priority at the top 5%
hline1 <- quantile(AS_combined$priority,1) # genetic priority at the top
p <- ggplot(AS_combined, aes(factor(PS), priority))
p + geom_violin(trim=T,adjust=0.5) + scale_y_log10() + ylab("AS-specific genetic priority") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=hline,color="darkgreen",linetype="dotdash") + geom_vline(xintercept=c(2,5)+0.5,color="transparent",linetype="dotdash") + geom_segment(aes(x=2.5,xend=2.5,y=hline,yend=hline1),color="red",linetype="dotdash") + geom_segment(aes(x=4.5,xend=4.5,y=hline,yend=hline1),color="red",linetype="dotdash")

A horizontal line is for genetic priority at the top 5%, and two vertical lines separate well-annotated genes, moderately annotated genes, and genes seldom annotated. Therefore genes can be divided into 4 areas:

Top-right: 43 easy targets/genes receiving high genetic priority and being well-annotated. They are as expected considering sufficient existing knowledge in support. The followup risk is low but the novelty is compromised.
Top-middle: 126 a2maid targets/genes receiving high genetic priority and being moderately annotated. They might represent attractive targets as both the risk and novelty are at the twilight zone. The origin of the wording a2maid is explained here.¹
Top-left: 586 novel targets/genes receiving high genetic priority but with seldom annotations. Its great novelty comes the high risk to follow-up.
Bottom: targets unlikely related to AS (maybe other immune diseases).

The 43 easy targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
IL6R	interleukin 6 receptor	0.00747	5	0
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	0.00717	4	1
CARD9	caspase recruitment domain family, member 9	0.00608	4	0
TYK2	tyrosine kinase 2	0.00396	4	0
IFIH1	interferon induced with helicase C domain 1	0.00390	4	0
CD27	CD27 molecule	0.00331	4	0
FCGR3A	Fc fragment of IgG, low affinity IIIa, receptor (CD16a)	0.00277	4	0
IL12B	interleukin 12B	0.00255	5	0
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	0.00252	4	0
IL2RA	interleukin 2 receptor, alpha	0.00250	5	1
ICAM1	intercellular adhesion molecule 1	0.00183	4	0
NCF4	neutrophil cytosolic factor 4, 40kDa	0.00166	4	0
AIRE	autoimmune regulator	0.00096	4	0
ADAR	adenosine deaminase, RNA-specific	0.00090	4	1
HLA-B	major histocompatibility complex, class I, B	0.00075	4	0
HLA-A	major histocompatibility complex, class I, A	0.00067	5	0
NOTCH1	notch 1	0.00064	4	0
B2M	beta-2-microglobulin	0.00061	5	0
IFNG	interferon, gamma	0.00023	4	0
STAT4	signal transducer and activator of transcription 4	0.00021	4	0
IL12A	interleukin 12A	0.00021	4	0
JAK2	Janus kinase 2	0.00019	5	0
IL1B	interleukin 1, beta	0.00015	4	0
IL2	interleukin 2	0.00014	4	0
IL10	interleukin 10	0.00014	5	0
NFKB2	nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)	0.00014	5	0
JAK3	Janus kinase 3	0.00014	4	0
MALT1	MALT1 paracaspase	0.00013	4	0
STAT3	signal transducer and activator of transcription 3 (acute-phase response factor)	0.00013	4	0
IL1RN	interleukin 1 receptor antagonist	0.00012	5	0
ITGB2	integrin, beta 2 (complement component 3 receptor 3 and 4 subunit)	0.00011	5	0
PIK3R1	phosphoinositide-3-kinase, regulatory subunit 1 (alpha)	0.00011	5	0
CBL	Cbl proto-oncogene, E3 ubiquitin protein ligase	0.00011	4	0
PIK3CA	phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha	0.00011	4	1
IL6	interleukin 6	0.00011	5	0
NFKBIA	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha	0.00011	5	0
CD3E	CD3e molecule, epsilon (CD3-TCR complex)	0.00010	5	0
BCL10	B-cell CLL/lymphoma 10	0.00009	4	0
IL7R	interleukin 7 receptor	0.00008	5	0
AKT1	v-akt murine thymoma viral oncogene homolog 1	0.00008	5	0
FASLG	Fas ligand (TNF superfamily, member 6)	0.00008	4	0
FOS	FBJ murine osteosarcoma viral oncogene homolog	0.00008	4	0
RAF1	Raf-1 proto-oncogene, serine/threonine kinase	0.00008	4	0

The top 10 a2maid targets/genes are:

Target.Symbol	Target.Name	priority	PS
ERAP1	endoplasmic reticulum aminopeptidase 1	0.03276	2
IL23R	interleukin 23 receptor	0.01823	3
NPEPPS	aminopeptidase puromycin sensitive	0.00670	2
CACNA1S	calcium channel, voltage-dependent, L type, alpha 1S subunit	0.00605	2
FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	0.00557	2
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	0.00543	2
FCGR2B	Fc fragment of IgG, low affinity IIb, receptor (CD32)	0.00450	3
ITGB3	integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)	0.00401	3
TBX21	T-box 21	0.00395	2
NOS2	nitric oxide synthase 2, inducible	0.00388	3

The top 5 novel targets/genes are:

Target.Symbol	Target.Name	priority	PS
CAST	calpastatin	0.08460	1
ERAP2	endoplasmic reticulum aminopeptidase 2	0.03626	0
B3GNT2	UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 2	0.02077	0
LNPEP	leucyl/cystinyl aminopeptidase	0.01696	1
DDX39B	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39B	0.01284	1

Label the gene network with annotation prediction

The nodes/genes in AS-specific gene network are color-coded by annotation additive predictor scores.

g <- AS_net
evi <- AS_combined[,3]
names(evi) <- rownames(AS_combined)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Nodes/genes evidenced by an individual predictor and/or nominated by TPN members are highlighted in pink. Here, only nodes with additive predictor scores >=4 are labeled by gene symbols.

g <- AS_net
evi <- AS_combined[, c(4:8,12)]
data <- evi[V(g)$name,]
vertex.label <- V(g)$name
vertex.label[apply(data[,1:5],1,sum)<=3] <- ''
visNetMul(g, data=data, glayout=layout_(g, with_kk()), vertex.label=vertex.label, vertex.shape="sphere", colormap="white-magenta", newpage=F, colorbar=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, vertex.size=8, mtext.cex=0.7,border.color="black",zlim=c(0,1), mtext.side=3, mtext.adj=0)

6.4 Integration for Spondyloarthritis

Recall: SA genes prioritised by Pi are stored in the data frame SA_genes, and the data frame iPS for annotation predictor scores for all genes stored in iSymbol or iGenes (see above).

# find genes with annotation predictors  
ind <- match(rownames(SA_genes), iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine SA_genes iPS iGenes iNominated
SA_combined <- cbind(SA_genes[,c(1,4)], genes_ap)

The top 20 SA-specific genes along with annotation predictors and TPN nominations are listed below:

name	priority	PS	OMIM	Phenotype	Function	Pathway	Druggable	Nominated
CAST	0.02679	1	0	0	0	0	1	0
DDX39B	0.02230	1	0	0	0	0	1	0
ATF6B	0.01918	0	0	0	0	0	0	0
NFKBIL1	0.01806	1	1	0	0	0	0	0
IER3	0.01174	0	0	0	0	0	0	0
ERAP2	0.01148	0	0	0	0	0	0	0
ERAP1	0.01037	2	0	0	1	1	0	0
TRAF3IP2	0.01030	1	0	1	0	0	0	0
MDC1	0.00998	0	0	0	0	0	0	0
TNIP1	0.00967	0	0	0	0	0	0	0
ANXA6	0.00925	0	0	0	0	0	0	0
VARS2	0.00913	0	0	0	0	0	0	1
LCE3D	0.00903	0	0	0	0	0	0	0
LCE3A	0.00849	0	0	0	0	0	0	0
LCE3B	0.00818	0	0	0	0	0	0	0
LCE3C	0.00802	0	0	0	0	0	0	0
LCE3E	0.00751	0	0	0	0	0	0	0
TYK2	0.00672	4	1	1	0	1	1	0
B3GNT2	0.00657	0	0	0	0	0	0	0
CNPY2	0.00652	0	0	0	0	0	0	0

Comparing genetic priority and annotation prediction

This violin plot displays density (the violin shape) of genes receiving SA-specific genetic priority versus annotation predictor scores (general indicator of immune-relatedness).

hline <- quantile(SA_combined$priority,0.95) # genetic priority at the top 5%
hline1 <- quantile(SA_combined$priority,1) # genetic priority at the top
p <- ggplot(SA_combined, aes(factor(PS), priority))
p + geom_violin(trim=T,adjust=0.5) + scale_y_log10() + ylab("Spondyloarthritis-specific genetic priority") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=hline,color="darkgreen",linetype="dotdash") + geom_vline(xintercept=c(2,5)+0.5,color="transparent",linetype="dotdash") + geom_segment(aes(x=2.5,xend=2.5,y=hline,yend=hline1),color="red",linetype="dotdash") + geom_segment(aes(x=4.5,xend=4.5,y=hline,yend=hline1),color="red",linetype="dotdash")

Top-right: 49 easy targets/genes receiving high genetic priority and being well-annotated. They are as expected considering sufficient existing knowledge in support. The followup risk is low but the novelty is compromised.
Top-middle: 102 a2maid targets/genes receiving high genetic priority and being moderately annotated. They might represent attractive targets as both the risk and novelty are at the twilight zone.
Top-left: 604 novel targets/genes receiving high genetic priority but with seldom annotations. Its great novelty comes the high risk to follow-up.
Bottom: targets unlikely related to Spondyloarthritis (maybe other immune diseases).

The 49 easy targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
TYK2	tyrosine kinase 2	0.00672	4	0
IL12B	interleukin 12B	0.00518	5	0
HLA-DQA1	major histocompatibility complex, class II, DQ alpha 1	0.00392	4	0
ICAM1	intercellular adhesion molecule 1	0.00390	4	0
IL6R	interleukin 6 receptor	0.00243	5	0
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	0.00233	4	1
NFKBIA	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha	0.00217	5	0
DDX58	DEAD (Asp-Glu-Ala-Asp) box polypeptide 58	0.00215	4	0
CARD9	caspase recruitment domain family, member 9	0.00194	4	0
IFIH1	interferon induced with helicase C domain 1	0.00114	4	0
CD27	CD27 molecule	0.00105	4	0
STAT3	signal transducer and activator of transcription 3 (acute-phase response factor)	0.00094	4	0
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	0.00091	4	0
FCGR3A	Fc fragment of IgG, low affinity IIIa, receptor (CD16a)	0.00089	4	0
IL2RA	interleukin 2 receptor, alpha	0.00085	5	1
C4A	complement component 4A (Rodgers blood group)	0.00056	4	0
NCF4	neutrophil cytosolic factor 4, 40kDa	0.00055	4	0
TNF	tumor necrosis factor	0.00044	4	0
CFB	complement factor B	0.00042	5	0
AIRE	autoimmune regulator	0.00034	4	0
MAPK1	mitogen-activated protein kinase 1	0.00031	4	0
ADAR	adenosine deaminase, RNA-specific	0.00031	4	1
PSMB8	proteasome (prosome, macropain) subunit, beta type, 8	0.00030	5	0
HLA-B	major histocompatibility complex, class I, B	0.00027	4	0
HLA-A	major histocompatibility complex, class I, A	0.00025	5	0
B2M	beta-2-microglobulin	0.00024	5	0
NOTCH1	notch 1	0.00022	4	0
IKBKG	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma	0.00022	5	0
STAT5B	signal transducer and activator of transcription 5B	0.00019	4	0
PTPN11	protein tyrosine phosphatase, non-receptor type 11	0.00019	4	0
HLA-DQB1	major histocompatibility complex, class II, DQ beta 1	0.00017	4	0
CHUK	conserved helix-loop-helix ubiquitous kinase	0.00015	4	0
IL12A	interleukin 12A	0.00014	4	0
NFKB2	nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)	0.00013	5	0
IKBKB	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta	0.00013	5	0
IFNG	interferon, gamma	0.00013	4	0
STAT4	signal transducer and activator of transcription 4	0.00012	4	0
JAK2	Janus kinase 2	0.00012	5	0
MALT1	MALT1 paracaspase	0.00012	4	0
CD40	CD40 molecule, TNF receptor superfamily member 5	0.00012	5	0
ITGB2	integrin, beta 2 (complement component 3 receptor 3 and 4 subunit)	0.00011	5	0
HLA-DRB1	major histocompatibility complex, class II, DR beta 1	0.00011	4	0
IL2	interleukin 2	0.00011	4	0
IL1B	interleukin 1, beta	0.00011	4	0
RAG1	recombination activating gene 1	0.00010	4	0
PRKDC	protein kinase, DNA-activated, catalytic polypeptide	0.00010	4	1
EGFR	epidermal growth factor receptor	0.00009	4	0
FOS	FBJ murine osteosarcoma viral oncogene homolog	0.00009	4	0
JAK3	Janus kinase 3	0.00009	4	0

The top 10 a2maid targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
ERAP1	endoplasmic reticulum aminopeptidase 1	0.01037	2	0
ICAM3	intercellular adhesion molecule 3	0.00594	2	0
IL23R	interleukin 23 receptor	0.00584	3	0
TNFAIP3	tumor necrosis factor, alpha-induced protein 3	0.00537	2	1
FYN	FYN proto-oncogene, Src family tyrosine kinase	0.00450	3	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	0.00409	2	0
HLA-E	major histocompatibility complex, class I, E	0.00344	2	0
NOS2	nitric oxide synthase 2, inducible	0.00296	3	0
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	0.00243	3	1
NPEPPS	aminopeptidase puromycin sensitive	0.00213	2	0

The top 5 novel targets/genes are:

Target.Symbol	Target.Name	priority	PS
CAST	calpastatin	0.02679	1
DDX39B	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39B	0.02230	1
ATF6B	activating transcription factor 6 beta	0.01918	0
NFKBIL1	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor-like 1	0.01806	1
IER3	immediate early response 3	0.01174	0

Label the gene network with annotation prediction

The nodes/genes in Spondyloarthritis-specific gene network are color-coded by annotation additive predictor scores.

g <- SA_net
evi <- SA_combined[,3]
names(evi) <- rownames(SA_combined)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Nodes/genes evidenced by an individual predictor and/or nominated by TPN members are highlighted in pink. Here, only nodes with additive predictor scores >=4 are labeled by gene symbols.

g <- SA_net
evi <- SA_combined[, c(4:8,12)]
data <- evi[V(g)$name,]
vertex.label <- V(g)$name
vertex.label[apply(data[,1:5],1,sum)<=3] <- ''
visNetMul(g, data=data, glayout=layout_(g, with_kk()), vertex.label=vertex.label, vertex.shape="sphere", colormap="white-magenta", newpage=F, colorbar=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, vertex.size=8, mtext.cex=0.7,border.color="black",zlim=c(0,1), mtext.side=3, mtext.adj=0)

6.5 Integration for Systemic Lupus Erythematosus

Recall: SLE genes prioritised by Pi are stored in the data frame SLE_genes, and the data frame iPS for annotation predictor scores for all genes stored in iSymbol or iGenes (see above).

# find genes with annotation predictors  
ind <- match(rownames(SLE_genes), iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine SLE_genes iPS iGenes iNominated
SLE_combined <- cbind(SLE_genes[,c(1,4)], genes_ap)

The top 20 SLE-specific genes along with annotation predictors and TPN nominations are listed below:

name	priority	PS	OMIM	Phenotype	Function	Pathway	Druggable	Nominated
ATF6B	0.04358	0	0	0	0	0	0	0
NELFE	0.02335	0	0	0	0	0	0	0
VARS2	0.02029	0	0	0	0	0	0	1
ITGAM	0.01884	3	1	1	1	0	0	1
PBX2	0.01783	0	0	0	0	0	0	0
PYCARD	0.01753	2	0	0	1	1	0	0
CUEDC2	0.01746	0	0	0	0	0	0	0
STAT4	0.01707	4	1	1	0	1	1	0
HLA-DPB1	0.01576	2	1	0	0	1	0	0
IRF5	0.01540	3	1	1	0	1	0	1
TNPO3	0.01533	1	1	0	0	0	0	0
CLIC1	0.01527	0	0	0	0	0	0	0
PSMB9	0.01482	3	0	0	1	1	1	0
RNF114	0.01427	0	0	0	0	0	0	0
C2	0.01354	2	0	0	1	1	0	0
LTA	0.01303	3	1	0	0	1	1	0
SKIV2L	0.01275	0	0	0	0	0	0	0
NCF2	0.01219	3	1	0	1	1	0	0
HNRNPM	0.01217	0	0	0	0	0	0	0
SMG7	0.01137	0	0	0	0	0	0	0

Comparing genetic priority and annotation prediction

This violin plot displays density (the violin shape) of genes receiving SLE-specific genetic priority versus annotation predictor scores (general indicator of immune-relatedness).

hline <- quantile(SLE_combined$priority,0.95) # genetic priority at the top 5%
hline1 <- quantile(SLE_combined$priority,1) # genetic priority at the top
p <- ggplot(SLE_combined, aes(factor(PS), priority))
p + geom_violin(trim=T,adjust=0.5) + scale_y_log10() + ylab("SLE-specific genetic priority") + xlab("Annotation predictor scores (general indicator of immune-relatedness)") + theme_bw() +
theme(axis.title.y=element_text(size=14,color="blue"), axis.title.x=element_text(size=14,color="blue")) + geom_hline(yintercept=hline,color="darkgreen",linetype="dotdash") + geom_vline(xintercept=c(2,5)+0.5,color="transparent",linetype="dotdash") + geom_segment(aes(x=2.5,xend=2.5,y=hline,yend=hline1),color="red",linetype="dotdash") + geom_segment(aes(x=4.5,xend=4.5,y=hline,yend=hline1),color="red",linetype="dotdash")

Top-right: 47 easy targets/genes receiving high genetic priority and being well-annotated. They are as expected considering sufficient existing knowledge in support. The followup risk is low but the novelty is compromised.
Top-middle: 108 a2maid targets/genes receiving high genetic priority and being moderately annotated. They might represent attractive targets as both the risk and novelty are at the twilight zone.
Top-left: 600 novel targets/genes receiving high genetic priority but with seldom annotations. Its great novelty comes the high risk to follow-up.
Bottom: targets unlikely related to SLE (maybe other immune diseases).

The 47 easy targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
STAT4	signal transducer and activator of transcription 4	0.01707	4	0
C4A	complement component 4A (Rodgers blood group)	0.00709	4	0
HLA-DQA1	major histocompatibility complex, class II, DQ alpha 1	0.00570	4	0
PTPN22	protein tyrosine phosphatase, non-receptor type 22 (lymphoid)	0.00365	4	2
TYK2	tyrosine kinase 2	0.00217	4	0
CFB	complement factor B	0.00208	5	0
IRF8	interferon regulatory factor 8	0.00172	4	0
IFIH1	interferon induced with helicase C domain 1	0.00157	4	0
IRF7	interferon regulatory factor 7	0.00139	4	0
CDKN1B	cyclin-dependent kinase inhibitor 1B (p27, Kip1)	0.00123	4	0
TNF	tumor necrosis factor	0.00113	4	0
HRAS	Harvey rat sarcoma viral oncogene homolog	0.00107	4	0
IL10	interleukin 10	0.00093	5	0
FCGR3A	Fc fragment of IgG, low affinity IIIa, receptor (CD16a)	0.00084	4	0
PNP	purine nucleoside phosphorylase	0.00081	4	0
IL12A	interleukin 12A	0.00071	4	0
ICAM1	intercellular adhesion molecule 1	0.00065	4	0
MAPK1	mitogen-activated protein kinase 1	0.00042	4	0
HLA-DQB1	major histocompatibility complex, class II, DQ beta 1	0.00030	4	0
CHUK	conserved helix-loop-helix ubiquitous kinase	0.00028	4	0
CARD9	caspase recruitment domain family, member 9	0.00026	4	0
MASP2	mannan-binding lectin serine peptidase 2	0.00026	5	0
CIITA	class II, major histocompatibility complex, transactivator	0.00024	4	1
C1QA	complement component 1, q subcomponent, A chain	0.00021	4	0
HLA-DRB1	major histocompatibility complex, class II, DR beta 1	0.00020	4	0
BCL2	B-cell CLL/lymphoma 2	0.00020	5	0
CYBB	cytochrome b-245, beta polypeptide	0.00020	5	0
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	0.00019	4	1
PSTPIP1	proline-serine-threonine phosphatase interacting protein 1	0.00019	4	0
MEFV	Mediterranean fever	0.00018	4	0
IKBKG	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma	0.00017	5	0
NLRP3	NLR family, pyrin domain containing 3	0.00016	4	1
NFKB1	nuclear factor of kappa light polypeptide gene enhancer in B-cells 1	0.00014	4	0
MYD88	myeloid differentiation primary response 88	0.00014	4	0
IL12B	interleukin 12B	0.00013	5	0
IL1B	interleukin 1, beta	0.00013	4	0
ITGB2	integrin, beta 2 (complement component 3 receptor 3 and 4 subunit)	0.00013	5	0
PDGFRA	platelet-derived growth factor receptor, alpha polypeptide	0.00013	4	0
CREBBP	CREB binding protein	0.00012	4	1
C3	complement component 3	0.00012	5	0
PSMB8	proteasome (prosome, macropain) subunit, beta type, 8	0.00012	5	0
IL2RA	interleukin 2 receptor, alpha	0.00012	5	1
PRKDC	protein kinase, DNA-activated, catalytic polypeptide	0.00012	4	1
IFNG	interferon, gamma	0.00011	4	0
IKBKB	inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta	0.00011	5	0
ITK	IL2-inducible T-cell kinase	0.00010	5	0
TLR2	toll-like receptor 2	0.00010	4	0

The top 10 a2maid targets/genes are:

Target.Symbol	Target.Name	priority	PS	Nominated
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	0.01884	3	1
PYCARD	PYD and CARD domain containing	0.01753	2	0
HLA-DPB1	major histocompatibility complex, class II, DP beta 1	0.01576	2	0
IRF5	interferon regulatory factor 5	0.01540	3	1
PSMB9	proteasome (prosome, macropain) subunit, beta type, 9	0.01482	3	0
C2	complement component 2	0.01354	2	0
LTA	lymphotoxin alpha	0.01303	3	0
NCF2	neutrophil cytosolic factor 2	0.01219	3	0
STAT1	signal transducer and activator of transcription 1, 91kDa	0.00620	3	0
TNFAIP3	tumor necrosis factor, alpha-induced protein 3	0.00352	2	1

The top 5 novel targets/genes are:

Target.Symbol	Target.Name	priority	Nominated
ATF6B	activating transcription factor 6 beta	0.04358	0
NELFE	negative elongation factor complex member E	0.02335	0
VARS2	valyl-tRNA synthetase 2, mitochondrial	0.02029	1
PBX2	pre-B-cell leukemia homeobox 2	0.01783	0
CUEDC2	CUE domain containing 2	0.01746	0

Label the gene network with annotation prediction

The nodes/genes in SLE-specific gene network are color-coded by annotation additive predictor scores.

g <- SLE_net
evi <- SLE_combined[,3]
names(evi) <- rownames(SLE_combined)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

Nodes/genes evidenced by an individual predictor and/or nominated by TPN members are highlighted in pink. Here, only nodes with additive predictor scores >=4 are labeled by gene symbols.

g <- SLE_net
evi <- SLE_combined[, c(4:8,12)]
data <- evi[V(g)$name,]
vertex.label <- V(g)$name
vertex.label[apply(data[,1:5],1,sum)<=3] <- ''
visNetMul(g, data=data, glayout=layout_(g, with_kk()), vertex.label=vertex.label, vertex.shape="sphere", colormap="white-magenta", newpage=F, colorbar=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, vertex.size=8, mtext.cex=0.7,border.color="black",zlim=c(0,1), mtext.side=3, mtext.adj=0)

7 Cross-disease comparisons

7.1 Gene-level comparisons

Recall: disease-centric genetic prioritisation for genes by Pi are stored respectively in three data frames, that is, AS_genes for Ankylosing Spondylitis, SA_genes for Spondyloarthritis and SLE_genes for Systemic Lupus Erythematosus as exemplars. The data frame iPS stores annotation predictor scores for all genes, the vector iNominated for nominated genes, and iSymbol or iGenes for gene info.

# Ankylosing Spondylitis (AS)
ind <- match(iSymbol, AS_genes$name)
df_AS <- AS_genes[ind,]
# Spondyloarthritis (SA)
ind <- match(iSymbol, SA_genes$name)
df_SA <- SA_genes[ind,]
# Systemic Lupus Erythematosus (SLE)
ind <- match(iSymbol, SLE_genes$name)
df_SLE <- SLE_genes[ind,]

# Combine into a data frame 'df_rank' and `df_priority`
## rank
df_rank <- cbind(AS=df_AS$rank, SA=df_SA$rank, SLE=df_SLE$rank)
## priority
df_priority <- cbind(AS=df_AS$priority, SA=df_SA$priority, SLE=df_SLE$priority)
rownames(df_rank) <- rownames(df_priority) <- iSymbol

Venn diagram of the top 200 genes across diseases

ntop <- 200
data <- list()
data$AS <- iSymbol[which(df_rank[,1] <= ntop)]
data$SA <- iSymbol[which(df_rank[,2] <= ntop)]
data$SLE <- iSymbol[which(df_rank[,3] <= ntop)]
category.names <- c('Ankylosing Spondylitis (AS)', 'Spondyloarthritis (SA)', 'Systemic Lupus Erythematosus (SLE)')
vp <- venn.diagram(x=data, filename=NULL, fill=c("skyblue","pink1","mediumorchid"), category.names=names(data), margin=0.15)
grid.draw(vp)

The 14 genes common to three diseases (along with annotation additive predictor scores and how many times being nominated by TPN members) are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
UBE2L3	ubiquitin-conjugating enzyme E2L 3	1	1
IFIH1	interferon induced with helicase C domain 1	4	0
TYK2	tyrosine kinase 2	4	0
FCGR2B	Fc fragment of IgG, low affinity IIb, receptor (CD32)	3	0
FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	2	0
ICAM3	intercellular adhesion molecule 3	2	0
DDX39B	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39B	1	0
LTBR	lymphotoxin beta receptor (TNFR superfamily, member 3)	1	0
NFKBIL1	nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor-like 1	1	0
CDC37	cell division cycle 37	0	0
FDX1L	ferredoxin 1-like	0	0
HSPA6	heat shock 70kDa protein 6 (HSP70B’)	0	0
RAVER1	ribonucleoprotein, PTB-binding 1	0	0
RLTPR	RGD motif, leucine rich repeats, tropomodulin domain and proline-rich containing	0	0

Venn diagram of the top 200 genes in any diseases vs 714 TPN nominated genes

ntop <- 200
data <- list()
data$AS <- iSymbol[which(df_rank[,1] <= ntop)]
data$SA <- iSymbol[which(df_rank[,2] <= ntop)]
data$SLE <- iSymbol[which(df_rank[,3] <= ntop)]
data$Prioritised <- base::Reduce(union, data[1:3])
data$Nominated <- iSymbol[which(iNominated>0)]
category.names <- c('Prioritised', 'Nominated')
vp <- venn.diagram(x=data[4:5], filename=NULL, fill=c("red","orange"), category.names=category.names, margin=0.15, cat.pos=1)
grid.draw(vp)

The 30 genes common to disease-specific genetic prioritisations and TPN nominations (along with annotation additive predictor scores and how many times being nominated by TPN members) are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
PXK	PX domain containing serine/threonine kinase	1	4
PTPN22	protein tyrosine phosphatase, non-receptor type 22 (lymphoid)	4	2
IL2RA	interleukin 2 receptor, alpha	5	1
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	4	1
BLK	BLK proto-oncogene, Src family tyrosine kinase	3	1
IRF5	interferon regulatory factor 5	3	1
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	3	1
TNFAIP3	tumor necrosis factor, alpha-induced protein 3	2	1
BANK1	B-cell scaffold protein with ankyrin repeats 1	1	1
GPR65	G protein-coupled receptor 65	1	1
IL23A	interleukin 23, alpha subunit p19	1	1
KAT8	K(lysine) acetyltransferase 8	1	1
SCNN1A	sodium channel, non voltage gated 1 alpha subunit	1	1
SLC11A1	solute carrier family 11 (proton-coupled divalent metal ion transporter), member 1	1	1
SLC15A4	solute carrier family 15 (oligopeptide transporter), member 4	1	1
SPHK2	sphingosine kinase 2	1	1
UBE2L3	ubiquitin-conjugating enzyme E2L 3	1	1
APOBEC4	apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 4 (putative)	0	1
BRWD1	bromodomain and WD repeat domain containing 1	0	1
CCDC101	coiled-coil domain containing 101	0	1
MBTPS2	membrane-bound transcription factor peptidase, site 2	0	1
ORAI3	ORAI calcium release-activated calcium modulator 3	0	1
PARP11	poly (ADP-ribose) polymerase family, member 11	0	1
RAB5A	RAB5A, member RAS oncogene family	0	1
RAB5B	RAB5B, member RAS oncogene family	0	1
RIOK2	RIO kinase 2	0	1
SLC9A8	solute carrier family 9, subfamily A (NHE8, cation proton antiporter 8), member 8	0	1
SMPD2	sphingomyelin phosphodiesterase 2, neutral membrane (neutral sphingomyelinase)	0	1
TET3	tet methylcytosine dioxygenase 3	0	1
VARS2	valyl-tRNA synthetase 2, mitochondrial	0	1

7.2 Pathway-level comparisons

Recall: disease-centric genetic prioritisation for pathways by Pi are stored respectively in three data frames, that is, AS_pathways for Ankylosing Spondylitis, SA_pathways for Spondyloarthritis and SLE_pathways for Systemic Lupus Erythematosus as exemplars.

Load all pathways into a data frame iPathway:

org.Hs.egMsigdbC2CPall <- xRDataLoader(RData.customised='org.Hs.egMsigdbC2CPall', RData.location=RData.location)
iPathway <- org.Hs.egMsigdbC2CPall$set_info[, c(1,2)]

# Ankylosing Spondylitis (AS)
ind <- match(rownames(iPathway), rownames(AS_pathways))
df_AS <- AS_pathways[ind,]
# Spondyloarthritis (SA)
ind <- match(rownames(iPathway), rownames(SA_pathways))
df_SA <- SA_pathways[ind,]
# Systemic Lupus Erythematosus (SLE)
ind <- match(rownames(iPathway), rownames(SLE_pathways))
df_SLE <- SLE_pathways[ind,]

# Combine into a data frame 'df_adjp'
## adjusted p-values
df_adjp <- cbind(AS=df_AS$adjp, SA=df_SA$adjp, SLE=df_SLE$adjp)
rownames(df_adjp) <- iPathway$name

This barplot displays prioritised pathways for three diseases, in which horizontal lines are used to indicate three groups of pathways prioritised:

Top panel: pathways common to all diseases, including IL23-mediated signaling events, IL27-mediated signaling events.
Middle panel: pathways common to any two diseases.
Bottom panel: pathways unique to individual diseases.

7.3 Network-level comparisons

Recall: the gene network identified by Pi are stored respectively in three data frames, that is, AS_net for Ankylosing Spondylitis, SA_net for Spondyloarthritis and SLE_net for Systemic Lupus Erythematosus as exemplars. The data frame iPS stores annotation predictor scores for all genes, the vector iNominated for nominated genes, and iSymbol or iGenes for gene info.

# Ankylosing Spondylitis (AS)
ind <- match(iSymbol, V(AS_net)$name)
df_AS <- V(AS_net)$name[ind]
# Spondyloarthritis (SA)
ind <- match(iSymbol, V(SA_net)$name)
df_SA <- V(SA_net)$name[ind]
# Systemic Lupus Erythematosus (SLE)
ind <- match(iSymbol, V(SLE_net)$name)
df_SLE <- V(SLE_net)$name[ind]

# Combine into a data frame 'df_net'
df_net <- cbind(AS=df_AS, SA=df_SA, SLE=df_SLE)
rownames(df_net) <- iSymbol

Venn diagram of network genes across diseases

data <- list()
data$AS <- iSymbol[!is.na(df_net[,1])]
data$SA <- iSymbol[!is.na(df_net[,2])]
data$SLE <- iSymbol[!is.na(df_net[,3])]
category.names <- c('Ankylosing Spondylitis (AS)', 'Spondyloarthritis (SA)', 'Systemic Lupus Erythematosus (SLE)')
vp <- venn.diagram(x=data, filename=NULL, fill=c("skyblue","pink1","mediumorchid"), category.names=names(data), margin=0.2)
grid.draw(vp)

The common part of the gene network shared by AS, SA and SLE

# identify common parts of SLE-network and AS-network. 
net_AS_SA_SLE <- graph.intersection(AS_net, SA_net, SLE_net, keep.all.vertices=F)

# append genes with info about annotation predictors  
g <- net_AS_SA_SLE
ind <- match(V(g)$name, iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine iGenes iPS iNominated
g_info <- genes_ap[,c(8:9,1,10)]
colnames(g_info)[3] <- 'Annotated'
rownames(g_info) <- g_info[,1]

The 3 genes common to three diseases (along with annotation additive predictor scores and how many times being nominated by TPN members) are listed below:

Target.Symbol	Target.Name	Annotated
TYK2	tyrosine kinase 2	4
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2
CDC37	cell division cycle 37	0

The network nodes/genes shared by three diseases are color-coded by annotation additive predictor scores.

# visualise intersected network part with nodes/genes color-encoded by annotation predictors
evi <- g_info[,3]
names(evi) <- rownames(g_info)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

The common part of the gene network shared by AS and SA

# identify common parts of AS-network and SA-network. 
net_AS_SA <- graph.intersection(AS_net, SA_net, keep.all.vertices=F)

# append genes with info about annotation predictors  
g <- net_AS_SA
ind <- match(V(g)$name, iSymbol)
genes_ap <- cbind(iPS[ind,], iGenes[ind,], Nominated=iNominated[ind])
genes_ap[is.na(genes_ap)] <- 0 # NA means no annotation predictors
# combine iGenes iPS iNominated
g_info <- genes_ap[,c(8:9,1,10)]
colnames(g_info)[3] <- 'Annotated'
rownames(g_info) <- g_info[,1]

The common 21 genes along with annotation predictors and TPN nominations are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
TNFRSF1A	tumor necrosis factor receptor superfamily, member 1A	4	1
SCNN1A	sodium channel, non voltage gated 1 alpha subunit	1	1
IL12B	interleukin 12B	5	0
IL6R	interleukin 6 receptor	5	0
ICAM1	intercellular adhesion molecule 1	4	0
TYK2	tyrosine kinase 2	4	0
IL23R	interleukin 23 receptor	3	0
ITGB3	integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)	3	0
NOS2	nitric oxide synthase 2, inducible	3	0
ERAP1	endoplasmic reticulum aminopeptidase 1	2	0
ICAM3	intercellular adhesion molecule 3	2	0
ICAM4	intercellular adhesion molecule 4 (Landsteiner-Wiener blood group)	2	0
NPEPPS	aminopeptidase puromycin sensitive	2	0
TBX21	T-box 21	2	0
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2	0
CAST	calpastatin	1	0
IL12RB2	interleukin 12 receptor, beta 2	1	0
LNPEP	leucyl/cystinyl aminopeptidase	1	0
CDC37	cell division cycle 37	0	0
MRPL4	mitochondrial ribosomal protein L4	0	0
RUNX3	runt-related transcription factor 3	0	0

The network nodes/genes shared by AS and SA are color-coded by annotation additive predictor scores.

# visualise intersected network part with nodes/genes color-encoded by annotation predictors
evi <- g_info[,3]
names(evi) <- rownames(g_info)
pattern <- evi[V(g)$name]
zmax <- max(pattern)
xVisNet(g, pattern=pattern, glayout=layout_(g, with_kk()), vertex.shape="sphere", colormap="white-darkred", newpage=F, edge.arrow.size=0.3, vertex.label.color="blue", vertex.label.dist=0.35, vertex.label.font=2, zlim=c(0,zmax))

The common part of the gene network shared by SLE and SA

The common 10 genes along with annotation predictors and TPN nominations are listed below:

Target.Symbol	Target.Name	Annotated	Nominated
ITGAM	integrin, alpha M (complement component 3 receptor 3 subunit)	3	1
TYK2	tyrosine kinase 2	4	0
FCGR2A	Fc fragment of IgG, low affinity IIa, receptor (CD32)	2	0
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2	0
ETS1	v-ets avian erythroblastosis virus E26 oncogene homolog 1	1	0
SQSTM1	sequestosome 1	1	0
TRIM56	tripartite motif containing 56	1	0
CDC37	cell division cycle 37	0	0
ITGAX	integrin, alpha X (complement component 3 receptor 4 subunit)	0	0
RPL34	ribosomal protein L34	0	0

The network nodes/genes shared by SLE and SA are color-coded by annotation additive predictor scores.

The common part of the gene network shared by SLE and AS

The common 4 genes along with annotation predictors and TPN nominations are listed below:

Target.Symbol	Target.Name	Annotated
TYK2	tyrosine kinase 2	4
UBA52	ubiquitin A-52 residue ribosomal protein fusion product 1	2
SOCS1	suppressor of cytokine signaling 1	1
CDC37	cell division cycle 37	0

The network nodes/genes shared by SLE and AS are color-coded by annotation additive predictor scores.

8 References

Below is a list of references that this work stands on:

1000 Genomes Project Consortium. 2012. “An integrated map of genetic variation from 1,092 human genomes.” Nature 135 (V): 0–9. doi:10.1038/nature11632.

Ashburner, M, C A Ball, J A Blake, D Botstein, H Butler, J M Cherry, A P Davis, et al. 2000. “Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.” Nat Genet 25 (1): 25–29. doi:10.1038/75556.

Cerami, E. G., B. E. Gross, E. Demir, I. Rodchenkov, O. Babur, N. Anwar, N. Schultz, G. D. Bader, and C. Sander. 2011. “Pathway Commons, a web resource for biological pathway data.” Nucleic Acids Research 39 (Database): D685–D690. doi:10.1093/nar/gkq1039.

Fairfax, Benjamin P, Peter Humburg, Seiko Makino, Vivek Naranbhai, Daniel Wong, Evelyn Lau, Luke Jostins, et al. 2014. “Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression.” Science (New York, N.Y.) 343 (March): 1246949. doi:10.1126/science.1246949.

Fang, Hai, and Julian Gough. 2014. “The ’dnet’ approach promotes emerging research on cancer patient survival.” Genome Medicine 6 (8): 64. doi:10.1186/s13073-014-0064-8.

Köhler, Sebastian, Sandra C Doelken, Christopher J Mungall, Sebastian Bauer, Helen V Firth, Isabelle Bailleul-Forestier, Graeme C M Black, et al. 2013. “The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data.” Nucleic Acids Research, November, 1–9. doi:10.1093/nar/gkt1026.

Schriml, L M, C Arze, S Nadendla, Y W Chang, M Mazaitis, V Felix, G Feng, and W A Kibbe. 2012. “Disease Ontology: a backbone for disease semantic integration.” Nucleic Acids Res 40 (Database issue): D940–6. doi:10.1093/nar/gkr972.

Smith, C L, and J T Eppig. 2009. “The Mammalian Phenotype Ontology: enabling robust annotation and comparative analysis.” Wiley Interdiscip Rev Syst Biol Med 1 (3): 390–99. doi:10.1002/wsbm.44.

Szklarczyk, Damian, Andrea Franceschini, Stefan Wyder, Kristoffer Forslund, Davide Heller, Jaime Huerta-cepas, Milan Simonovic, et al. 2015. “STRING v10 : protein – protein interaction networks , integrated over the tree of life.” Nucleic Acids Res 43 (Database): D447–D452. doi:10.1093/nar/gku1003.

Welter, Danielle, Jacqueline MacArthur, Joannella Morales, Tony Burdett, Peggy Hall, Heather Junkins, Alan Klemm, et al. 2014. “The NHGRI GWAS Catalog, a curated resource of SNP-trait associations.” Nucleic Acids Research 42 (D1): 1001–6. doi:10.1093/nar/gkt1229.

The wording a2maid comes from the mermaid (describing the maid of the sea having a fish’s tail), and instead of the maid of the sea, replacing with a2 turns to be the maid of our lab having two PhD students Anna Sanniti and Alicia Lledo Lara.↩

Networking Genetic Evidence to Prioritise Drug Targets at the Gene, Pathway and Network Level

Tue May 10 17:42:42 2016

1 Overview

2 Web app

3 Workflow

4 R functions

4.1 xRWR

4.2 xPrioritiser

4.3 xPrioritiserGenes

4.4 xPrioritiserSNPs

4.5 xPrioritiserManhattan

4.6 xPrioritiserPathways

4.7 xPrioritiserSubnet

5 Showcases

5.1 Ankylosing Spondylitis

5.1.1 Gene-level prioritisation

5.1.2 Pathway-level prioritisation

5.1.3 Network-level prioritisation

5.2 Spondyloarthritis

5.2.1 Gene-level prioritisation

5.2.2 Pathway-level prioritisation

5.2.3 Network-level prioritisation

5.3 Systemic Lupus Erythematosus

5.3.1 Gene-level prioritisation

5.3.2 Pathway-level prioritisation

5.3.3 Network-level prioritisation

5.4 Dupuytren’s disease (DD)

5.4.1 Gene-level prioritisation

5.4.2 Pathway-level prioritisation

5.4.3 Network-level prioritisation

6 Ontology annotations & TPN nominations

6.1 Annotation predictors

6.2 TPN nominations

6.3 Integration for Ankylosing Spondylitis

6.4 Integration for Spondyloarthritis

6.5 Integration for Systemic Lupus Erythematosus

7 Cross-disease comparisons

7.1 Gene-level comparisons

7.2 Pathway-level comparisons

7.3 Network-level comparisons

8 References